size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.9946552
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix" "array"
head( m )
x1 x2 x3
[1,] -0.09672203 0.01472721 -0.1236171
[2,] 0.42877095 0.35786697 0.3594459
[3,] -2.91999172 -2.94920412 -3.0263494
[4,] -0.71985614 -0.84495266 -0.9525388
[5,] -0.80705213 -0.78402544 -0.7408337
[6,] -0.32867769 -0.37454895 -0.4183782
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.989 0.021 0.992 0.031 0.018 0.993
y3 0.989 1.000 0.026 0.983 0.038 0.022 0.989
x2 0.021 0.026 1.000 -0.009 0.991 0.998 0.021
y1 0.992 0.983 -0.009 1.000 0.004 -0.008 0.984
x1 0.031 0.038 0.991 0.004 1.000 0.994 0.027
x3 0.018 0.022 0.998 -0.008 0.994 1.000 0.016
y2 0.993 0.989 0.021 0.984 0.027 0.016 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
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